#add parent dir to find package. Only needed for source code build, pip install doesn't need it.
import os, inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(os.path.dirname(currentdir))
os.sys.path.insert(0,parentdir)

import gym
import numpy as np
import pybullet as p
import pybulletgym.envs
import time


def relu(x):
    return np.maximum(x, 0)


class SmallReactivePolicy:
    "Simple multi-layer perceptron policy, no internal state"
    def __init__(self, observation_space, action_space):
        assert weights_dense1_w.shape == (observation_space.shape[0], 128)
        assert weights_dense2_w.shape == (128, 64)
        assert weights_final_w.shape  == (64, action_space.shape[0])

    @staticmethod
    def act(ob):
        x = ob
        x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
        x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
        x = np.dot(x, weights_final_w) + weights_final_b
        return x


def main():
    print("create env")
    env = gym.make("HopperPyBulletEnv-v0")
    env.render(mode="human")
    pi = SmallReactivePolicy(env.observation_space, env.action_space)

    env.reset()
    for i in range (p.getNumBodies()):
        print(p.getBodyInfo(i))
        if p.getBodyInfo(i)[1].decode() == "hopper":
           torsoId = i
           print("found torso")
           print(p.getNumJoints(torsoId))
           for j in range (p.getNumJoints(torsoId)):
              print(p.getJointInfo(torsoId,j))  # LinkState(torsoId,j))
    while 1:
        frame = 0
        score = 0
        restart_delay = 0
        #disable rendering during reset, makes loading much faster
        obs = env.reset()
        print("frame")
        while 1:
            time.sleep(0.02)
            a = pi.act(obs)
            obs, r, done, _ = env.step(a)
            score += r
            frame += 1
            distance = 5
            yaw = 0
            humanPos = p.getLinkState(torsoId,4)[0]
            p.resetDebugVisualizerCamera(distance, yaw, -20, humanPos)

            still_open = env.render("human")
            if still_open is None:
                return
            if not done:
                continue
            if restart_delay == 0:
                print("score=%0.2f in %i frames" % (score, frame))
                restart_delay = 60*2  # 2 sec at 60 fps
            else:
                restart_delay -= 1
                if restart_delay == 0:
                    break

weights_dense1_w = np.array([
[ +0.6734, +1.1364, +0.6117, +0.5185, +0.5099, -0.2038, -0.0045, -0.1448, +0.5081, +1.1685, -0.7904, -0.4623, +0.0027, -0.0473, -0.1144, +0.5095, -0.1913, +0.2021, +0.3485, +0.1104, -0.4992, +0.5207, -0.1013, -0.6947, +0.1624, +0.3533, -0.2485, -0.0012, +0.1674, +0.1253, +1.5485, +0.3576, +0.8236, +0.7361, +0.6604, -0.0834, +0.1212, -0.8404, -0.8337, +0.3709, -0.4218, -0.1011, -1.1418, -0.0554, +0.6676, +0.4739, -0.2105, +0.3187, -0.4321, -0.7018, +0.1845, +0.2525, +0.0205, +0.9391, +0.6123, +0.6868, +0.5116, +0.3483, +0.1148, +0.6747, -0.1590, +0.1879, +0.4836, +0.1997, +0.5105, -0.2695, +0.0645, +0.5566, +0.0502, +0.2292, -0.4234, -0.3778, -0.7639, -0.6084, -0.0375, -0.4799, +0.6465, -0.4097, +0.1091, -0.0681, -0.1813, +0.3625, +0.6067, +0.1837, +0.1600, -0.1706, +0.0531, -0.3710, +0.1320, +0.5035, +0.1106, +0.9955, -0.2657, +0.6051, -0.2525, -0.9118, -0.6031, +0.2025, -0.2774, +0.3985, -0.0809, +0.3601, -0.0410, -0.5067, +0.4987, +0.4126, -0.1393, -0.6596, +0.8182, -0.2352, -0.9337, +0.1438, +0.3871, -0.1844, -0.4010, -0.3338, +0.1597, +0.2381, -0.4403, +0.5105, +1.0354, -0.1503, +0.2731, +0.6555, +0.2048, +0.4932, -0.0067, +0.0413],
[ -0.0031, -0.2183, +0.2275, -0.4836, +0.1347, +0.3581, +0.0921, +0.1900, -0.1721, +0.0150, -0.2146, +0.5346, +0.2407, -0.0791, +0.5115, -0.0763, +0.2025, +0.2792, +0.4087, +0.2082, +0.0086, -0.2742, +0.3703, +0.4297, -0.3141, -0.3896, +0.4101, -0.3967, -0.2173, -0.0532, +0.2521, +0.3938, -0.3727, +0.6136, +0.0902, -0.4617, -0.3266, -0.2273, +0.0031, +0.4605, +0.1221, +0.3603, -0.1018, +0.1839, +0.3198, -0.1404, -0.1751, -0.1030, -0.5847, +0.2526, -0.1280, +0.1644, +0.2988, -0.1793, -0.2120, +0.3398, -0.4582, +0.0758, -0.2848, -0.1537, -0.2718, -0.2207, +0.0109, +0.1374, -0.0281, -0.0494, -0.0093, +0.0098, +0.2691, +0.1589, +0.2483, +0.1394, +0.2531, +0.3884, +0.0740, -0.4444, +0.1441, +0.0768, +0.2154, +0.4015, -0.2109, -0.0985, -0.1066, -0.0167, -0.0204, -0.2345, +0.3131, +0.1026, +0.1873, +0.2644, +0.2253, -0.0173, +0.1014, +0.0781, +0.0455, -0.0489, -0.3541, -0.1257, -0.1248, +0.4402, -0.0682, -0.3045, +0.1751, +0.0397, -0.3392, -0.2758, +0.1542, -0.0460, -0.0622, +0.2255, +0.2442, +0.2070, +0.3175, -0.0913, -0.2772, +0.7036, -0.0876, +0.0907, +0.3794, +0.4784, -0.5111, +0.0982, -0.0145, +0.0397, -0.3429, -0.2727, -0.1384, +0.0512],
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])

weights_dense2_b = np.array([ +0.0266, +0.3395, +0.0968, +0.0097, +0.0443, -0.1722, -0.2537, +0.1501, +0.0233, -0.0174, +0.1783, -0.0513, -0.1531, -0.2145, +0.0299, -0.1589, +0.1143, +0.1132, -0.2700, -0.0481, +0.2876, +0.1824, +0.2418, +0.1009, -0.1451, +0.1637, +0.1703, +0.0336, -0.1405, -0.0162, -0.1158, +0.1691, -0.0223, -0.1828, +0.2352, +0.0209, -0.1362, -0.2636, +0.1271, -0.0936, -0.1436, +0.0606, +0.1311, -0.0989, -0.2877, +0.2593, +0.0781, -0.1118, -0.1264, +0.2478, +0.2398, -0.1515, +0.0959, -0.0821, -0.1740, +0.1116, -0.0682, +0.1743, +0.1746, -0.1950, -0.0951, +0.2190, -0.0294, +0.1950])

weights_final_w = np.array([
[ -0.6826, +0.0812, +0.5225],
[ -0.2533, +0.7043, +0.6677],
[ +0.0684, +0.6807, +0.4726],
[ +0.4605, +0.3736, -0.3187],
[ +0.4913, +0.0049, +0.4186],
[ +0.3066, +0.3756, -0.3588],
[ -0.3078, +0.3643, -0.0335],
[ -0.5433, +0.5926, -0.1254],
[ -0.2532, +0.7950, -0.1380],
[ -0.6121, -0.3012, +0.0149],
[ +0.3181, +0.0973, +0.1705],
[ +0.2236, -0.1020, +0.3147],
[ -0.0038, -0.3193, +0.1068],
[ +0.3799, -0.0215, -0.0715],
[ +0.3440, -0.2333, -0.1001],
[ +0.1852, +0.7445, +0.4295],
[ +0.4888, -0.0469, -0.1287],
[ +0.0297, -0.3913, -0.5198],
[ -0.0092, -0.1013, +0.2193],
[ +0.2590, +0.0846, +0.0886],
[ +0.0744, +0.4771, -0.3553],
[ +0.2019, +0.1670, -0.1630],
[ -0.4549, -0.6127, +0.5951],
[ -0.2582, -0.6401, -0.6541],
[ +0.0248, +0.0646, +0.1598],
[ +0.2109, -0.0794, -0.2444],
[ +0.2435, -0.4159, -0.2927],
[ -0.0713, +0.2441, -0.4683],
[ -0.0737, +0.2075, +0.4609],
[ +0.3240, -0.4084, -0.4554],
[ +0.3463, +0.4490, -0.2689],
[ +0.5252, -0.3714, +0.2808],
[ +0.5561, +0.1137, -0.0258],
[ +0.6168, +0.0587, -0.1059],
[ -0.0737, -0.6782, -0.4729],
[ -0.6030, -0.2577, +0.4194],
[ +0.4061, -0.1966, +0.2603],
[ -0.1265, +0.3940, +0.0447],
[ +0.2460, +0.3328, +0.1654],
[ -0.1878, +0.6672, +0.0333],
[ +0.5618, -0.1013, -0.2911],
[ +0.8250, -0.0788, -0.3731],
[ +0.2661, -0.2775, +0.2854],
[ -0.1036, +0.2179, -0.6408],
[ -0.5371, -0.7837, +0.1141],
[ +0.4754, -0.0923, +0.1714],
[ +0.0448, +0.4901, -0.1578],
[ +0.8797, +0.3316, +0.4349],
[ +0.1514, -0.4129, -0.3333],
[ +0.2847, +0.2285, +0.1408],
[ +0.0045, +0.3333, +0.0694],
[ +0.1553, -0.5147, +0.0549],
[ -1.0765, -0.5130, +0.1472],
[ +0.0808, -0.2612, +0.2591],
[ +0.5104, +0.3062, +0.3979],
[ -0.0758, -0.4512, -0.4422],
[ +0.3236, +0.4479, -0.1759],
[ +0.2181, -0.0028, +0.5615],
[ -0.0254, -0.1924, +0.4608],
[ -0.1566, +0.5634, -0.2498],
[ -0.0042, +0.5403, +0.0272],
[ +0.1206, +0.3941, -1.1469],
[ +0.7642, -1.0114, +0.4905],
[ +0.5071, +0.2270, +0.0046]
])

weights_final_b = np.array([ +0.4868, -0.0987, -0.0946])

if __name__ == "__main__":
    main()
